Complex deep space probe task planning method based on event constraint network

Abstract

The invention discloses a complex deep space probe task planning method based on an event constraint network, and the method comprises the steps: obtaining a successive execution relation between flight events, and converting the successive execution relation between the flight events into a directed vector; connecting the flight events into an event constraint network according to the directed vector; converting the event constraint network into an event constraint network adjacency matrix; searching a multi-node event and a boundary node event through the event constraint network adjacency matrix, and listing the multi-node event and the boundary node event as key events; establishing an inter-event time constraint inequality; obtaining a time constraint inequality of the key event according to the time constraint inequality between the events; and judging whether the time constraint inequality of the key event is a null set, if not, determining that the intersection is the execution time range of the flight events, and arranging the flight events. According to the invention, the problem of planning difficulty caused by many flight events and complex constraints among the events in the flight control process is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of deep space probe flight control, and in particular relates to a complex deep space probe mission planning method based on an event constraint network. Background Art

[0002] Most of the existing deep space probes are complex reconfigurable systems, and their control planning process is complex and has many constraints. Existing mission planning methods for complex systems are mostly focused on the design and search of state space, with a small amount of timeline planning involved in the search process, and most of them are focused on the mission planning of the state space itself. Mission planning based on state space is mostly to find an executable state solution space, but in the actual flight control process, after determining the mission requirements, the solution space of the state mission planning has been determined, and more often it is based on instruction-based events as the basic elements, and the event execution time as the drive to form an event control sequence. After the existing state space-based planning algorithm is completed, it is also necessary to consider the mapping relationship between the state space and the event. Sometimes this process cannot even meet the constraint control and planning between events.

[0003] In the process of mission planning for complex deep space probe systems, there are many flight events and complex constraints between events. Existing flight control methods have poor ability to judge the correctness of constraint control between events, and often cannot effectively detect constraint conflicts between events, resulting in planning errors. Summary of the invention

[0004] The technical problem solved by the present invention is: to overcome the shortcomings of the prior art and provide a complex deep space probe mission planning method based on an event constraint network, which solves the planning difficulty caused by the large number of flight events and the complex constraints between events during the flight control process, and the planning effect can be directly applied to the operation sequence of the flight control, which is more practical.

[0005] The purpose of the present invention is achieved through the following technical solutions: a complex deep space probe mission planning method based on an event constraint network, comprising: obtaining the execution sequence relationship between flight events, and converting the execution sequence relationship between flight events into a directed vector; according to the directed vector, connecting the flight events into an event constraint network; converting the event constraint network into an event constraint network adjacency matrix; finding multi-node events and boundary node events through the event constraint network adjacency matrix, and listing the multi-node events and boundary node events as key events; establishing a time constraint inequality between events through constraint analysis between events; obtaining the time constraint inequality of the key event according to the time constraint inequality between events; judging whether the time constraint inequality of the key event is an empty set, if not an empty set, the intersection is the execution time range of the flight event, and the flight events are arranged.

[0006] In the above-mentioned complex deep space probe mission planning method based on event constraint network, the sequential execution relationship between flight events includes the sequential execution relationship between flight events and the execution time interval constraints between flight events; wherein, the execution time interval constraints between flight events include the time constraints caused by the measurement and control arc, the constraints caused by the instruction execution interval and the time constraints caused by the equipment working time requirements.

[0007] In the above-mentioned complex deep space probe mission planning method based on event constraint network, in the event constraint network adjacency matrix, the positive and negative signs represent the direction of the vector, the value pointed to in each vertical column is positive, and the value pointed to other events is negative; the value pointed to in each horizontal row is negative, and the value pointed to other events is positive.

[0008] In the above-mentioned complex deep space probe mission planning method based on event constraint network, the multi-node event is: the flight event is a subsequent execution event of multiple events, or a pre-execution event of multiple events; the boundary node event is: the flight event has no pre-execution event, or the event has no subsequent execution event, usually the beginning or end of an event execution segment.

[0009] In the above-mentioned complex deep space probe mission planning method based on event constraint network, searching for multi-node events through the event constraint network adjacency matrix includes: nodes with two or more +1 or -1 in the rows or columns of the event constraint network adjacency matrix; searching for boundary node events through the event constraint network adjacency matrix includes: nodes with only a single symbol in the rows or columns of the event constraint network adjacency matrix, that is, they do not contain +1 and -1 at the same time.

[0010] In the above-mentioned complex deep space probe mission planning method based on event constraint network, the constraint analysis between events includes: command issuance constraints in the tracking and control arc, command interval constraints caused by command reception and processing, and equipment working sequence constraints.

[0011] In the above-mentioned complex deep space probe mission planning method based on event constraint network, the command constraint caused by the tracking and control arc is converted into executing the command sending event B after time T after the execution of the tracking and control arc start event A, that is, TB≥TA+T, or executing the command sending event B time T before the execution of the tracking and control arc end event A, that is, TB≤TA-T; the command interval constraint caused by command reception and processing is converted into the command time interval T, that is, TB≥TA+T for the command sending event A and the command sending event B; the equipment working sequence constraint is converted into the execution of event B after time T after equipment 1 executes event A, that is, TB≥TA+T.

[0012] In the above-mentioned complex deep space probe mission planning method based on event constraint network, the time constraint inequality of the key event is obtained according to the time constraint inequality between events, including: for the key event, the time constraint inequality of the key event is obtained by adding the inequalities forward and backward in sequence.

[0013] In the above-mentioned complex deep space probe mission planning method based on event constraint network, the arrangement of flight events includes: firstly arranging the flight events according to the minimum time of the execution time range; if the minimum time of multiple flight events is the same time, then arranging them in order according to the importance of the flight events, with important events given priority.

[0014] A complex deep space probe mission planning system based on an event constraint network comprises: a first module for obtaining the execution sequence relationship between flight events and converting the execution sequence relationship between flight events into a directed vector; a second module for connecting flight events into an event constraint network according to the directed vector; a third module for converting the event constraint network into an event constraint network adjacency matrix; a fourth module for finding multi-node events and boundary node events through the event constraint network adjacency matrix, and listing the multi-node events and boundary node events as key events; a fifth module for establishing a time constraint inequality between events through constraint analysis between events; a sixth module for obtaining a time constraint inequality for a key event according to the time constraint inequality between events; and a seventh module for judging whether the time constraint inequality for a key event is an empty set. If it is not an empty set, the intersection is the execution time range of the flight event, and the flight events are arranged.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] The present invention directly acts on execution events, and the planning effect can be directly applied to the operation sequence of flight control, which is more practical. By converting the inter-event constraints into inter-event time constraints and vector direction constraints, it is helpful to simplify the model dimension, facilitate modeling and simplify the solution process. And the event execution time is used as the direct output of the mission planning, and the executable nature of the output content is stronger. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0018] Figure 1 It is a flow chart of a complex deep space probe mission planning method based on an event constraint network provided by an embodiment of the present invention;

[0019] Figure 2 This is an example diagram of an event constraint network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] Figure 1 FIG. 1 is a flow chart of a complex deep space probe mission planning method based on an event constraint network provided by an embodiment of the present invention. Figure 1 As shown, the complex deep space probe mission planning method based on event constraint network includes:

[0022] Step S1: Obtain the execution order relationship between flight events, and convert the execution order relationship between flight events into a directed vector;

[0023] Step S2: connecting the flight events into an event constraint network according to the directed vectors;

[0024] Step S3: converting the event constraint network into an event constraint network adjacency matrix;

[0025] Step S4: finding multi-node events and boundary node events through the event constraint network adjacency matrix, and listing the multi-node events and boundary node events as key events;

[0026] Step S5: establishing an inter-event time constraint inequality through inter-event constraint analysis;

[0027] Step S6: obtaining the time constraint inequality of the key event according to the time constraint inequality between events;

[0028] Step S7: Determine whether the time constraint inequality of the key event is an empty set. If it is not an empty set, the intersection is the execution time range of the flight event, and the flight events are arranged.

[0029] The method further includes: in step S7, if it is an empty set, adjusting the execution relationship or condition between events, and returning to step S1.

[0030] The task planning method of the present invention is implemented by the following technical solutions: obtaining the execution relationship of events, forming an event constraint network, forming an event constraint network adjacency matrix, finding multi-node events and boundary node events through the event constraint network adjacency matrix, and taking them as key events, converting the constraints between events into time inequalities, calculating the time inequalities of the constraints of key events, and finally performing inequality constraint judgment and determining the event time range to complete the event arrangement of the flight program. The specific process is as follows:

[0031] Step S1, obtaining the execution order relationship between events, and converting the execution order relationship between events into a directed vector with a module length of 1;

[0032] The time constraints of the execution intervals between events include time constraints caused by the measurement and control arc, constraints caused by the instruction execution intervals, time constraints caused by the equipment working time requirements, etc.

[0033] Step S2, connecting the events into an event constraint network through the execution vector relationship between the events.

[0034] Step S3, converting the event constraint network into an event constraint network adjacency matrix, wherein the positive and negative signs represent the direction of the vector, the value pointed to in each column is positive, and the value pointed to other events is negative; the value pointed to in each row is negative, and the value pointed to other events is positive.

[0035] Step S4, searching for multi-node events and boundary node events through the adjacency matrix, and listing the multi-node events and boundary node events as key events.

[0036] The multi-node event includes that the event is a subsequent execution event of multiple events, or is a pre-execution event of multiple events.

[0037] The boundary node event includes an event without a pre-execution event or an event without a subsequent execution event, and is usually the beginning or end of an event execution segment.

[0038] Step S401, the method of searching for multi-node events through the adjacency matrix is ​​that there are two or more nodes with +1 or -1 in the row / column of the adjacency matrix;

[0039] Step S402, the method of searching for boundary node events through the adjacency matrix is ​​that the adjacency matrix row / column only has nodes with a single symbol, that is, does not contain +1 and -1 at the same time.

[0040] Step S5, establishing an inter-event time constraint inequality model through inter-event constraint analysis.

[0041] The execution constraints between events should include: command issuance constraints in the measurement and control arc, command interval constraints caused by command reception and processing, and equipment working sequence constraints.

[0042] The method to convert the inter-event constraints into time inequalities is:

[0043] The command constraint caused by the measurement and control arc is converted into executing the command sending event B after time T after the measurement and control arc start event A is executed, that is, TB≥TA+T, or executing the command sending event B before time T before the measurement and control arc end event A is executed, that is, TB≤TA-T;

[0044] The instruction interval constraint caused by instruction reception and processing is converted into the time interval T for sending instructions, that is, for instruction sending event A and instruction sending event B, TB≥TA+T;

[0045] The equipment working sequence constraint is transformed into that equipment 1 executes event A and then executes event B after T, that is, TB ≥ TA + T;

[0046] Bidirectional inequality constraints for the same event should be avoided during the conversion process. For example, event B starts T1 time after event A and T2 time before event C. This should be converted into the inequality TB≥TA+T1 and TC≥TB+T2, rather than TB≥TA+T1 and TB≤TC-T2, to provide calculation conditions for subsequent modeling calculations.

[0047] Step S6, calculating the inequality constraints of multi-node events and boundary node events (i.e. key events). Including:

[0048] Step S601, for non-critical events (i.e., single-entry and single-exit network nodes), the time constraint is recursively determined by the time on the path to determine its compliance;

[0049] Step S602: for key events (ie, multi-node events and boundary node events), time constraint inequalities are obtained by sequentially adding inequalities forward and backward, and the execution time range is determined by the intersection of the time inequalities.

[0050] Step S7, determining whether the time constraint inequality of the key event is an empty set;

[0051] Step S701, after the intersection of the time inequality is determined, the nodes with an empty intersection do not meet the constraints, and it is necessary to return to step S1, focusing on the network nodes where the important events that do not meet the requirements are located, re-analyze the constraint relationship between events, adjust the event constraints, redesign the execution conditions of the events, or re-plan the execution order of the events.

[0052] Step S702: After the intersection of the time inequality is determined, the event nodes whose intersection is non-empty are nodes that meet the constraint, and the time inequality range is recorded, and the next flight procedure design work is performed according to the range.

[0053] Step S8, after the intersection of the time inequality is determined, if the intersection is not empty, the intersection is the execution time range of the event, and the flight events are arranged:

[0054] Step S801, firstly arranging events according to the minimum time of the time range;

[0055] Step S802: If the minimum time of multiple events is the same, the events are arranged in order according to their importance, with important events being given priority.

[0056] The specific implementation of this embodiment includes: obtaining the execution relationship of events, forming an event constraint network, forming an event constraint network adjacency matrix, finding multi-node events and boundary node events through the event constraint network adjacency matrix, and taking them as key events, converting the constraints between events into time inequalities, calculating the time inequalities of the constraints of key events, and finally performing inequality constraint judgment and determining the event time range to complete the event arrangement of the flight program. The execution process is as follows: Figure 1 The specific contents are as follows:

[0057] The task planning events described in this article refer to executable events composed of instructions and should be indivisible atomic events.

[0058] Step S1, obtaining the execution sequence relationship between events.

[0059] The execution relationship between events includes the sequential execution relationship between events and the execution time interval constraint between events.

[0060] The specific example of the execution order relationship between the events is as follows: for a single machine, event A is power on, event B is status setting, event C is detection start, event D is status setting before shutdown, and event E is shutdown. The execution order of events A, B, C, D, and E is: event A→event B→event C→event D→event E. Then the relationship vector between event A and event B is Relationship vector between event B and event C And so on.

[0061] Step S2, connecting the events into an event constraint network through the execution vector relationship between the events.

[0062] For example, Figure 2 As shown in the figure, a specific example is as follows: for a single machine, event A is power on, event B is status setting, event C is detection start, event D is status setting before shutdown, and event E is shutdown. For another single machine, event F is power on, event G is status setting, event H is device start working, and event I is shutdown. Event H needs to be executed x9 s after G is executed and x after C is executed. 10s to execute, event D requires event C to execute x4 s to execute, event I requires event H to work x 11 Event J is the start of the measurement and control arc (the start time is t0), and event K is the end of the measurement and control arc (the end time is te). All events described in this example must be executed within the measurement and control arc, starting x1 s after the measurement and control arc starts and ending x6 s before the measurement and control arc ends.

[0063] Step S3, converting the event constraint network into an event constraint network adjacency matrix.

[0064] The positive and negative signs represent the direction of the vector, the value of the vector being pointed to in each column is positive, and the value of the vector pointing to other events is negative; the value of the vector being pointed to in each row is negative, and the value of the vector pointing to other events is positive. The specific example is the same as step S2, as shown in Table 1 below.

[0065] Table 1 Event constraint network adjacency matrix

[0066]

[0067]

[0068] Step S4, searching for multi-node events and boundary node events through the adjacency matrix, and listing the multi-node events and boundary node events as key events.

[0069] The multi-node event includes that the event is a subsequent execution event of multiple events, or is a pre-execution event of multiple events.

[0070] The boundary node event includes an event without a pre-execution event or an event without a subsequent execution event, and is usually the beginning or end of an event execution segment.

[0071] Step S401, the method of searching for multi-node events through the adjacency matrix is ​​that there are two or more nodes with +1 or -1 in the row / column of the adjacency matrix;

[0072] Step S402, the method of searching for boundary node events through the adjacency matrix is ​​that the adjacency matrix row / column only has nodes with a single symbol, that is, does not contain +1 and -1 at the same time.

[0073] For example, in step 2 and step 3, the key events are event J, event C, event H and event K.

[0074] Step S5, establishing an inter-event time constraint inequality model through inter-event constraint analysis.

[0075] The execution constraints between events should include: command issuance constraints in the measurement and control arc, command interval constraints caused by command reception and processing, and equipment working sequence constraints.

[0076] The method to convert the inter-event constraints into time inequalities is:

[0077] The command constraint caused by the measurement and control arc is converted into executing the command sending event B after time T after the measurement and control arc start event A is executed, that is, TB≥TA+T, or executing the command sending event B before time T before the measurement and control arc end event A is executed, that is, TB≤TA-T;

[0078] The instruction interval constraint caused by instruction reception and processing is converted into the time interval T for sending instructions, that is, for instruction sending event A and instruction sending event B, TB≥TA+T;

[0079] The equipment working sequence constraint is transformed into that equipment 1 executes event A and then executes event B after T, that is, TB ≥ TA + T;

[0080] Bidirectional inequality constraints for the same event should be avoided during the conversion process. For example, event B starts T1 time after event A and T2 time before event C. This should be converted into the inequality TB≥TA+T1 and TC≥TB+T2, rather than TB≥TA+T1 and TB≤TC-T2, to provide calculation conditions for subsequent modeling calculations.

[0081] For the specific example listed in step 2, the following inequality can be obtained:

[0082] T A ≥T J +x1

[0083] T B ≥T A +x2

[0084] T C ≥T B +x3

[0085] T D ≥T C +x4

[0086] T E ≥T D +x5

[0087] T K ≥T E +x6

[0088] T F ≥T J +x7

[0089] T G ≥T F +x8

[0090] T H ≥T G +x9

[0091] T1≥T C +x 10

[0092] T I ≥T H +x 11

[0093] T K ≥T I +x 12

[0094] According to the above inequality, the adjacency matrix is ​​updated as shown in Table 2.

[0095] Table 2 Example of adjacency matrix generated based on event constraint time inequality

[0096]

[0097]

[0098] Step S6, by searching the adjacency matrix updated according to the inequality, the inequality constraints of the multi-node events and the boundary node events (i.e., key events) are calculated. Including:

[0099] Step S601, for non-critical events (i.e., single-entry and single-exit network nodes), the time constraint is recursively determined by the time on the path to determine its compliance;

[0100] Step S602: for key events (ie, multi-node events and boundary node events), time constraint inequalities are obtained by sequentially adding inequalities forward and backward, and the execution time range is determined by the intersection of the time inequalities.

[0101] The specific example is the same as step S2. The time inequality constraints for the key event C are:

[0102] Adding the inequalities forward one by one gives:

[0103] Right now,

[0104] Adding the inequalities backwards one by one gives:

[0105] Right now,

[0106] Right now,

[0107] This results in the time inequality constraint for the key event C:

[0108]

[0109] By analogy, we can get the inequality constraint of the key event H:

[0110]

[0111] The inequality constraints between key event J and key event K can be obtained:

[0112]

[0113] Step S7, determining whether the time constraint inequality of the key event is an empty set, includes:

[0114] Step S701, after the intersection of the time inequality is determined, the nodes with an empty intersection do not meet the constraints, and it is necessary to return to step S1, focusing on the network nodes where the important events that do not meet the requirements are located, re-analyze the constraint relationship between events, adjust the event constraints, redesign the execution conditions of the events, or re-plan the execution order of the events.

[0115] Step S702: After the intersection of the time inequality is determined, the event nodes whose intersection is non-empty are nodes that meet the constraint, and the time inequality range is recorded, and the next flight procedure design work is performed according to the range.

[0116] Step S8, after the intersection of the time inequality is determined, if the intersection is not empty, the intersection is the execution time range of the event, and the flight events are arranged:

[0117] Step S801, firstly arranging events according to the minimum time of the time range;

[0118] Step S802: If the minimum time of multiple events is the same time, the events are arranged in order according to their importance, with important events being given priority.

[0119] The present embodiment also provides a complex deep space probe mission planning system based on an event constraint network, the system comprising: a first module, used to obtain the chronological execution relationship between flight events, and convert the chronological execution relationship between flight events into a directed vector; a second module, used to connect the flight events into an event constraint network according to the directed vector; a third module, used to convert the event constraint network into an event constraint network adjacency matrix; a fourth module, used to find multi-node events and boundary node events through the event constraint network adjacency matrix, and list the multi-node events and boundary node events as key events; a fifth module, used to establish time constraint inequalities between events through constraint analysis between events; a sixth module, used to obtain time constraint inequalities for key events according to the time constraint inequalities between events; a seventh module, used to determine whether the time constraint inequality for key events is an empty set, if not an empty set, the intersection is the execution time range of the flight event, and the flight events are arranged.

[0120] This embodiment can directly plan and control flight events and their action moments. It also converts the constraints between events into an event constraint network model between events to detect event constraint conflicts in planning and effectively locate the event execution time range. This embodiment solves the problem of difficult planning due to the large number of flight events and complex constraints between events during the flight control process, and the planning effect can be directly applied to the operation sequence of flight control, which is more practical.

[0121] This embodiment directly acts on the execution event, and the planning effect can be directly applied to the operation sequence of flight control, which is more practical. By converting the inter-event constraints into inter-event time constraints and vector direction constraints, it is helpful to simplify the model dimension, facilitate modeling and simplify the solution process. And the event execution time is used as the direct output of the mission planning, and the executable content of the output content is stronger.

[0122] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A complex deep space probe mission planning method based on event constraint network, characterized by include: Obtaining the execution order relationship between flight events, and converting the execution order relationship between flight events into a directed vector; According to the directed vectors, the flight events are connected into an event constraint network; Convert the event constraint network into an event constraint network adjacency matrix; Find multi-node events and boundary node events through the event constraint network adjacency matrix, and list multi-node events and boundary node events as key events; Establish the time constraint inequality between events through constraint analysis between events; According to the time constraint inequality between events, the time constraint inequality of the key event is obtained; Determine whether the time constraint inequality of the key event is an empty set. If it is not an empty set, the intersection is the execution time range of the flight event, and the flight events are arranged.

2. The complex deep space probe mission planning method based on event constraint network according to claim 1 is characterized in that: The sequential execution relationship between flight events includes the sequential execution relationship between flight events and the execution time interval constraint between flight events; wherein, The execution time interval constraints between flight events include time constraints caused by the measurement and control arc, constraints caused by the instruction execution interval and time constraints caused by the equipment working time requirements.

3. The complex deep space probe mission planning method based on event constraint network according to claim 1 is characterized by: In the event constraint network adjacency matrix, the positive and negative signs represent the direction of the vector. In each vertical column, the direction pointed to is positive, and the direction pointed to other events is negative; in each horizontal row, the direction pointed to is negative, and the direction pointed to other events is positive.

4. The complex deep space probe mission planning method based on event constraint network according to claim 1 is characterized in that: The multi-node event is: the flight event is a subsequent execution event of multiple events, or a pre-execution event of multiple events; The boundary node event is: the flight event has no pre-execution event, or the event has no subsequent execution event, usually the beginning or end of an event execution section.

5. The complex deep space probe mission planning method based on event constraint network according to claim 1 is characterized by: Finding multi-node events through the event constraint network adjacency matrix includes: there are two or more +1 or -1 nodes in the row or column of the event constraint network adjacency matrix; Finding boundary node events through the event constraint network adjacency matrix includes: nodes with only a single symbol in the row or column of the event constraint network adjacency matrix, that is, not containing +1 and -1 at the same time.

6. The complex deep space probe mission planning method based on event constraint network according to claim 1, characterized in that: The constraint analysis between events includes: command issuance constraints in the measurement and control arc, command interval constraints caused by command reception and processing, and equipment working sequence constraints.

7. The complex deep space probe mission planning method based on event constraint network according to claim 6 is characterized by: The command constraint caused by the measurement and control arc is converted into executing the command sending event B after time T after the measurement and control arc start event A is executed, that is, TB≥TA+T, or executing the command sending event B before time T before the measurement and control arc end event A is executed, that is, TB≤TA-T; The instruction interval constraint caused by instruction reception and processing is converted into the time interval T for sending instructions, that is, for instruction sending event A and instruction sending event B, TB≥TA+T; The equipment working sequence constraint is transformed into equipment 1 executing event A and then executing event B after a period of time T, that is, TB ≥ TA + T.

8. The complex deep space probe mission planning method based on event constraint network according to claim 1 is characterized by: Obtaining the time constraint inequality of the key event according to the time constraint inequality between events includes: obtaining the time constraint inequality of the key event by sequentially adding the inequalities forward and backward for the key event.

9. The complex deep space probe mission planning method based on event constraint network according to claim 1, characterized in that: The flight events are arranged as follows: First, arrange the flight events according to the minimum time of the execution time range; If the minimum time of multiple flight events is the same, they will be arranged in order of importance, with important events given priority.

10. A complex deep space probe mission planning system based on event constraint network, characterized by include: The first module is used to obtain the execution order relationship between flight events and convert the execution order relationship between flight events into a directed vector; The second module is used to connect the flight events into an event constraint network according to the directed vectors; The third module is used to transform the event constraint network into an event constraint network adjacency matrix; The fourth module is used to find multi-node events and boundary node events through the event constraint network adjacency matrix, and list the multi-node events and boundary node events as key events; The fifth module is used to establish the time constraint inequality between events through the constraint analysis between events; The sixth module is used to obtain the time constraint inequality of the key event according to the time constraint inequality between events; The seventh module is used to determine whether the time constraint inequality of the key event is an empty set. If it is not an empty set, the intersection is the execution time range of the flight event, and the flight events are arranged.

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